Package index • markovchain

All functions

ExpectedTime(): Expected hitting time of a state in a continuous time Markov chain
HigherOrderMarkovChain-class: Higher order Markov Chains class
absorptionProbabilities(): Absorption probabilities
aggregateStates(): Aggregate a Markov chain's state space by Kullback-Leibler minimization
assessIndependence(): Test independence of consecutive states of an empirical sequence
autoLump(): Aggregate a Markov chain by spectral clustering
autoplot.markovchain(): Plot a Markov chain with ggplot2
birthDeath(): Build a birth-death Markov chain
blanden: Mobility between income quartiles
bootstrapProperties(): Bootstrap confidence intervals for properties of an estimated chain
closestReversible(): Closest reversible approximation of a Markov chain
committorAB(): Committor probabilities
compareChains(): Compare two Markov chains
conditionalDistribution(): Conditional distribution of the next state
craigsendi: CD4 cells counts on HIV Infects between zero and six month
ctmc-class dim,ctmc-method initialize,ctmc_method states,ctmc-method steadyStates,ctmc-method plot,ctmc,missing-method: Continuous time Markov chain class
ctmcFit: Fit a continuous time Markov chain
dirichletChain(): Markov chain from a Dirichlet process
divergenceRate(): Kullback-Leibler divergence rate between two Markov chains
entropyRate(): Entropy rate of a Markov chain
expectedRewards(): Expected cumulative reward of a Markov chain
expectedRewardsBeforeHittingA(): Expected reward collected before hitting a set of states
firstPassage(): First passage time distribution
firstPassageMultiple(): First passage time distribution of a set of states
fitFunction(): Markov chain approximating a continuous transition kernel by quadrature
fitHighOrderMultivarMC(): Fit a higher order multivariate Markov chain
fitHigherOrder() seq2freqProb() seq2matHigh(): Fit a higher order Markov chain
fitMTD(): Fit a mixture transition distribution (MTD) model
freq2Generator(): Generator matrix estimated from a matrix of transition frequencies
fundamentalMatrix(): Fundamental matrix of an absorbing Markov chain
gamblersRuin(): Build a gambler's ruin Markov chain
generatorToTransitionMatrix(): Transition matrix of the embedded jump chain
name(): Name of a Markov chain
higherOrderLogLik(): Log-likelihood, deviance and information criteria of a higher order Markov chain
higherOrderPredict() higherOrderSimulate(): Next-state probabilities and simulation for higher order Markov chains
hittingProbabilities(): Hitting probabilities of a Markov chain
holson: Holson data set
hommc-class hommc: Higher order multivariate Markov chain class
show(<hommc>): Function to display the details of hommc object
ictmc-class ictmc: An S4 class for representing Imprecise Continuous Time Markovchains
identityChain(): Identity Markov chain
impliedTimescales(): Implied timescales of a Markov chain
impreciseProbabilityatT(): Lower transition probability of an imprecise continuous time Markov chain
inferHyperparam(): Hyperparameters for Bayesian estimation of a Markov chain
is.CTMCirreducible(): Check whether a continuous time Markov chain is irreducible
is.TimeReversible(): Check whether a continuous time Markov chain is time reversible
is.accessible(): Check whether a state can be reached from another
is.irreducible(): Check whether a Markov chain is irreducible
is.lumpable(): Check exact lumpability of a Markov chain
is.regular(): Check if a DTMC is regular
is.reversible(): Check whether a Markov chain is reversible
is.stochasticallyMonotone(): Check whether a Markov chain is stochastically monotone
kemenyConstant(): Kemeny's constant of a Markov chain
kullback: Example from Kullback and Kupperman Tests for Contingency Tables
lazyChain(): Build a lazy version of a Markov chain
lump(): Aggregate a Markov chain over a partition
markovchain-class *,markovchain,markovchain-method *,markovchain,matrix-method *,markovchain,numeric-method *,matrix,markovchain-method *,numeric,markovchain-method ==,markovchain,markovchain-method !=,markovchain,markovchain-method absorbingStates,markovchain-method transientStates,markovchain-method recurrentStates,markovchain-method transientClasses,markovchain-method recurrentClasses,markovchain-method communicatingClasses,markovchain-method steadyStates,markovchain-method meanNumVisits,markovchain-method is.regular,markovchain-method is.irreducible,markovchain-method is.accessible,markovchain,character,character-method is.accessible,markovchain,missing,missing-method absorptionProbabilities,markovchain-method meanFirstPassageTime,markovchain,character-method meanFirstPassageTime,markovchain,missing-method meanAbsorptionTime,markovchain-method meanRecurrenceTime,markovchain-method conditionalDistribution,markovchain-method hittingProbabilities,markovchain-method canonicForm,markovchain-method coerce,data.frame,markovchain-method coerce,markovchain,data.frame-method coerce,table,markovchain-method coerce,markovchain,igraph-method coerce,markovchain,matrix-method coerce,markovchain,sparseMatrix-method coerce,sparseMatrix,markovchain-method coerce,matrix,markovchain-method coerce,Matrix,markovchain-method coerce,msm,markovchain-method coerce,msm.est,markovchain-method coerce,etm,markovchain-method dim,markovchain-method initialize,markovchain-method names<-,markovchain-method plot,markovchain,missing-method predict,markovchain-method print,markovchain-method show,markovchain-method summary,markovchain-method sort,markovchain-method t,markovchain-method [,markovchain,ANY,ANY,ANY-method ^,markovchain,numeric-method: Markov Chain class
markovchain markovchain-package: Easy Handling Discrete Time Markov Chains
.markovchainFitRcpp() createSequenceMatrix() markovchainFit(): Fit a discrete time Markov chain
markovchainList-class [[,markovchainList-method dim,markovchainList-method predict,markovchainList-method print,markovchainList-method show,markovchainList-method: Non-homogeneous discrete time Markov chain class
markovchainListFit(): Fit a non-homogeneous Markov chain
markovchainSequence(): Simulate a trajectory of a Markov chain
meanAbsorptionTime(): Mean absorption time
meanFirstPassageTime(): Mean first passage times of an irreducible Markov chain
meanNumVisits(): Mean number of visits to each state
meanRecurrenceTime(): Mean recurrence time
mergeWith(): Merge two Markov chains by convex combination of their transition matrices
mixingTime(): Mixing time of a Markov chain
multinomialConfidenceIntervals(): Simultaneous confidence intervals for transition probabilities
names(<markovchain>): Returns the states for a Markov chain object
noofVisitsDist(): Expected fraction of the first N steps spent in each state
normalizedEntropyRate(): Normalized entropy rate of a Markov chain
ones(): Returns an Identity matrix
plotSequence(): Plot state sequences of a Markov chain
populationGeneticsModel(): Build a population-genetics Markov chain (Moran or Wright-Fisher)
predictHommc(): Simulate from a higher order multivariate Markov chain
predictiveDistribution(): Bayesian predictive probability of new data
preproglucacon: Preprogluccacon DNA protein bases sequences
priorDistribution(): Prior density of a transition matrix
probabilityatT(): Transition probabilities of a continuous time Markov chain at time t
rain: Alofi island daily rainfall
randomMarkovChain(): Random Markov chain
rctmc(): Simulate a continuous time Markov chain
redistribute(): Evolution of a distribution over time
relaxationTime(): Relaxation time of a Markov chain
rmarkovchain(): Simulate from a homogeneous or non-homogeneous Markov chain
rouwenhorst(): Discretize an AR(1) process into a Markov chain (Rouwenhorst's method)
sales: Sales Demand Sequences
selectOrder(): Select the order of a Markov chain by information criteria
sensitivity(): Sensitivity of the stationary distribution to a state's transition row
`name<-`(): Set the name of a Markov chain
slem(): Second largest eigenvalue modulus (SLEM) of a Markov chain
spectralGap(): Spectral gap of a Markov chain
states(): States of a Markov chain
verifyMarkovProperty() assessOrder() verifyEmpiricalToTheoretical() verifyHomogeneity() assessStationarity(): Statistical tests for Markov chains
steadyStates(): Stationary distributions of a Markov chain
period() communicatingClasses() recurrentClasses() transientClasses() transientStates() recurrentStates() absorbingStates() canonicForm(): Structural analysis of a Markov chain
subchain(): Restrict a Markov chain to a subset of states
tauchen(): Discretize an AR(1) process into a Markov chain (Tauchen's method)
tauchenHussey(): Discretize an AR(1) process into a Markov chain (Tauchen-Hussey method)
timeCorrelations() timeRelaxations(): Time correlations and time relaxations of observed sequences
tm_abs: Single Year Corporate Credit Rating Transititions
toBoundedChain(): Apply a boundary condition to a Markov chain's first and last state
toDictionary() fromDictionary(): Represent a Markov chain as a plain R list
toDot() toMermaid(): Export the transition graph as Graphviz DOT or Mermaid text
toFile() fromFile(): Write or read a Markov chain to or from a file
toNthOrder(): Return the n-step transition chain
topologicalEntropy(): Topological entropy of a Markov chain
transition2Generator(): Generator matrix from a transition matrix
transitionProbability(): Transition probability between two states
urnModel(): Build an Ehrenfest urn model Markov chain
zeros(): Matrix to create zeros